minor black format updates

textattack/attack.py

@@ -81,8 +81,8 @@ def __init__(
         constraints: List[Union[Constraint, PreTransformationConstraint]],
         transformation: Transformation,
         search_method: SearchMethod,
-        transformation_cache_size=2**15,
-        constraint_cache_size=2**15,
+        transformation_cache_size=2 ** 15,
+        constraint_cache_size=2 ** 15,
     ):
         """Initialize an attack object.
 

textattack/attack_args.py

@@ -478,8 +478,8 @@ class _CommandLineAttackArgs:
     interactive: bool = False
     parallel: bool = False
     model_batch_size: int = 32
-    model_cache_size: int = 2**18
-    constraint_cache_size: int = 2**18
+    model_cache_size: int = 2 ** 18
+    constraint_cache_size: int = 2 ** 18
 
     @classmethod
     def _add_parser_args(cls, parser):

textattack/constraints/grammaticality/cola.py

@@ -43,7 +43,7 @@ def __init__(
 
         self.max_diff = max_diff
         self.model_name = model_name
-        self._reference_score_cache = lru.LRU(2**10)
+        self._reference_score_cache = lru.LRU(2 ** 10)
         model = AutoModelForSequenceClassification.from_pretrained(model_name)
         tokenizer = AutoTokenizer.from_pretrained(model_name)
         self.model = HuggingFaceModelWrapper(model, tokenizer)

textattack/constraints/grammaticality/language_models/google_language_model/alzantot_goog_lm.py

@@ -49,7 +49,7 @@ def __init__(self):
                 self.sess, self.graph, self.PBTXT_PATH, self.CKPT_PATH
             )
 
-        self.lm_cache = lru.LRU(2**18)
+        self.lm_cache = lru.LRU(2 ** 18)
 
     def clear_cache(self):
         self.lm_cache.clear()

textattack/constraints/grammaticality/part_of_speech.py

@@ -56,7 +56,7 @@ def __init__(
         self.language_nltk = language_nltk
         self.language_stanza = language_stanza
 
-        self._pos_tag_cache = lru.LRU(2**14)
+        self._pos_tag_cache = lru.LRU(2 ** 14)
         if tagger_type == "flair":
             if tagset == "universal":
                 self._flair_pos_tagger = SequenceTagger.load("upos-fast")

textattack/constraints/semantics/sentence_encoders/thought_vector.py

@@ -32,7 +32,7 @@ def __init__(self, embedding=None, **kwargs):
     def clear_cache(self):
         self._get_thought_vector.cache_clear()
 
-    @functools.lru_cache(maxsize=2**10)
+    @functools.lru_cache(maxsize=2 ** 10)
     def _get_thought_vector(self, text):
         """Sums the embeddings of all the words in ``text`` into a "thought
         vector"."""

textattack/goal_functions/goal_function.py

@@ -40,7 +40,7 @@ def __init__(
         use_cache=True,
         query_budget=float("inf"),
         model_batch_size=32,
-        model_cache_size=2**20,
+        model_cache_size=2 ** 20,
     ):
         validators.validate_model_goal_function_compatibility(
             self.__class__, model_wrapper.model.__class__

textattack/goal_functions/text/minimize_bleu.py

@@ -59,7 +59,7 @@ def extra_repr_keys(self):
             return ["maximizable", "target_bleu"]
 
 
-@functools.lru_cache(maxsize=2**12)
+@functools.lru_cache(maxsize=2 ** 12)
 def get_bleu(a, b):
     ref = a.words
     hyp = b.words

textattack/goal_functions/text/non_overlapping_output.py

@@ -38,12 +38,12 @@ def _get_score(self, model_output, _):
             return num_words_diff / len(get_words_cached(self.ground_truth_output))
 
 
-@functools.lru_cache(maxsize=2**12)
+@functools.lru_cache(maxsize=2 ** 12)
 def get_words_cached(s):
     return np.array(words_from_text(s))
 
 
-@functools.lru_cache(maxsize=2**12)
+@functools.lru_cache(maxsize=2 ** 12)
 def word_difference_score(s1, s2):
     """Returns the number of words that are non-overlapping between s1 and
     s2."""

textattack/metrics/attack_metrics/words_perturbed.py

@@ -31,7 +31,7 @@ def calculate(self, results):
         self.total_attacks = len(self.results)
         self.all_num_words = np.zeros(len(self.results))
         self.perturbed_word_percentages = np.zeros(len(self.results))
-        self.num_words_changed_until_success = np.zeros(2**16)
+        self.num_words_changed_until_success = np.zeros(2 ** 16)
         self.max_words_changed = 0
 
         for i, result in enumerate(self.results):